code: def launch(
      launcher,
      job_overrides: Sequence[Sequence[str]],
      initial_job_idx: int,
  ) -> Sequence[JobReturn]:
      """
      Args:
          launcher: Reference to the Launched subclass
          job_overrides: A List of List<String>, where each inner list is the arguments for one job run
          initial_job_idx: Initial job idx in batch
  
      Returns:
          A list of JobReturn objects.
      """
      # Needed for Hydra, lookup JoblibLauncher in Hydra
      setup_globals()
      assert launcher.config is not None
      assert launcher.task_function is not None
      assert launcher.hydra_context is not None
  
      configure_log(
          launcher.config.hydra.hydra_logging,
          launcher.config.hydra.verbose,
      )
      sweep_dir = Path(str(launcher.config.hydra.sweep.dir))
      sweep_dir.mkdir(parents=True, exist_ok=True)
  
      # Extraact the runner's config (its actually a DictConfig, but type is used for autocomplete)
      runner_cfg = launcher.runner  # type: ProcessLauncherConfig
  
      logging.info(
          "ProcessLauncher({}) is launching {} jobs".format(
              ",".join([f"{k}={v}" for k, v in runner_cfg.items()]),
              len(job_overrides),
          )
      )
      logging.info(
          "Launching jobs, sweep output dir : {}".format(sweep_dir)
      )
      for idx, overrides in enumerate(job_overrides):
          logging.info(
              "\t#{} : {}".format(
                  idx, " ".join(filter_overrides(overrides))
              )
          )
  
      # Needed by Hydra
      singleton_state = Singleton.get_state()
  
      # Process the runner's config to build up the multiplex config
      num_gpus = runner_cfg.get("num_gpus", -1)
      jobs_per_gpu = runner_cfg.get("jobs_per_gpu", 1)
  
      # Only GPUs are supported for now.
      if num_gpus <= 0:
          if torch.cuda.is_available():
              num_gpus = torch.cuda.device_count()
          else:
              raise ValueError(
                  f"{launcher.__class__.__name__} only supports GPU operations."
              )
  
      # Setup arguments for multiplex runner
      overrides = list(job_overrides)
      num_overrides = len(overrides)
  
      job_idx = 0
      batch_size = num_gpus * jobs_per_gpu
      gpu_idx = 0
  
      ret = []  # List of returned JobResult
      subprocess_list = []  # Buffer of subprocess
      results = []  # Buffer of JobResult
  
      # STD ERROR cache
      std_error_buffers = []  # type: List[List[str]]
      std_error_threads = []  # type: threading.Thread
  
      # Run over all job combinations
      while job_idx < num_overrides:
          # Fill up subprocess buffer while its size is smaller than multiplex batch size
          while len(subprocess_list) < batch_size:
              # If we run out of jobs, stop trying to submit more jobs
              if job_idx >= num_overrides:
                  break
  
              # Submit a job as a new process
              process, res, error_tup = execute_job(
                  initial_job_idx + job_idx,
                  overrides[job_idx],
                  launcher.hydra_context,
                  launcher.config,
                  singleton_state,
                  gpu_idx
                  % num_gpus,  # This will evenly distribute GPU load
              )
  
              # Store the subprocesses and JobResults
              subprocess_list.append(process)
              results.append(res)
  
              # Manage stderror thread data
              std_error_buffers.append(error_tup[0])
              std_error_threads.append(error_tup[1])
  
              job_idx += 1
              gpu_idx += 1
  
          # Poll for samples in batch to finish.
          if len(subprocess_list) > 0:
              finished_processes = [0] * len(subprocess_list)
  
              # Check if all processes are completed or not
              # This is busy waiting, this is actually quite necessary
              # Turns out that when you do proc.communicate(), you block all other threads immediately.
              # IE they may fill up their buffers entirely, and hang while they wait for the first thread
              # who called communicate() to finish its work or crash.
              # Effectively it entirely stops multiprocessing jobs or multiplexed runs.
              # Must poll and busy wait to keep threads alive, along with drain the pipes with thread buffers.
              while sum(finished_processes) < len(subprocess_list):
                  # Check all processes to make sure they have a retcode (doesnt matter yet if 0 or not)
                  for proc_idx, proc in enumerate(subprocess_list):
                      # poll() is cheaper op than communicate()
                      retcode = proc.poll()
  
                      if retcode is not None:
                          # Log that the process with some ID has finished
                          if finished_processes[proc_idx] == 0:
                              logging.info(
                                  f"Processed job : {len(ret) + proc_idx} :: Ret code = {retcode}"
                              )
  
                          finished_processes[proc_idx] = 1
  
                          # Join this thread and merge its stderror buffer
                          proc.wait()
                          std_error_threads[proc_idx].join()
                          error_data = std_error_buffers[proc_idx]
                          error_data = [
                              str(data, encoding="utf-8")
                              .encode("utf-8")
                              .decode("utf-8")
                              .encode("utf-8")
                              for data in error_data
                          ]
  
                          std_error_buffers[proc_idx] = error_data
  
                  time.sleep(1.0)
  
              # Process all the subprocess results
              for proc_idx, (proc, res) in enumerate(
                  zip(subprocess_list, results)
              ):
                  # Wait until completion of process
                  output, error = proc.communicate()
  
                  # 0 is for successful run
                  if proc.returncode == 0:
                      res.status = JobStatus.COMPLETED
                  else:
                      # > 0 is for error, log the error.
                      # Note: For the sake of efficiency while we log the error and raise an exception,
                      # It will only raise the 1st wrong job in all the jobs.
                      # If multiple jobs fail, it will still try to execute every job first before
                      # raising the error for the first one.
                      # This is done so that even if some jobs fail (say OOM or something),
                      # other jobs can still run.
                      err_buffer = std_error_buffers[proc_idx]
                      if isinstance(err_buffer, (list, tuple)):
                          err_string = ""
                          for err_line in err_buffer:
                              err_string = (
                                  err_string
                                  + f"{str(err_line, encoding='utf-8').encode('utf-8').decode('utf-8')}"
                              )
                      else:
                          err_string = err_buffer
  
                      error_msg = (
                          f"\nHyperparameter Arguments : {proc.args}\n"
                          f"Process Return code : {proc.returncode}\n"
                          f"Error Trace :\n"
                          f"{err_string}"
                      )
                      res.return_value = Exception(error_msg)
                      res.status = JobStatus.FAILED
  
                  logging.info(
                      f"Finished executing job : {len(ret)}. Return Code = {proc.returncode}"
                  )
                  ret.append(res)
  
              # Reset for next batch
              subprocess_list.clear()
              results.clear()
  
      return ret
  
imports: import copy
  import hashlib
  import os
  import subprocess
  import sys
  import threading
  import time
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Any, Dict, Optional, Sequence
  import torch
  from hydra.core.config_store import ConfigStore
  from hydra.core.hydra_config import HydraConfig
  from hydra.core.plugins import Plugins
  from hydra.core.singleton import Singleton
  from hydra.core.utils import JobReturn, JobStatus, configure_log, filter_overrides, setup_globals
  from hydra.plugins.launcher import Launcher
  from hydra.types import HydraContext, TaskFunction
  from omegaconf import DictConfig, OmegaConf, open_dict
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling a global function with example usage: launch(launcher, job_overrides, initial_job_idx) and returns: ret